Detection and control based on soil damage

ABSTRACT

A soil measure, such as a soil cone index, and a vehicle index indicating the amount of force the vehicle exerts on the ground as it travels over the ground, are obtained and compared to identify a soil damage score. The soil damage score can be mapped over a field and an agricultural vehicle can be controlled based upon the soil damage score.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines. Morespecifically, the present description relates to agricultural vehiclesthat have a load that varies as the agricultural vehicle travels acrossa field.

BACKGROUND

There are a wide variety of different types of agricultural vehicles.Some such vehicles include sprayers, seeders and planters, air seeders,harvesters, nutrient spreaders, baling equipment, etc. All of thesetypes of agricultural vehicles operate in a field and vary in weightover the course of the field.

The loads in these vehicles vary because the amount of material that isbeing gathered from the field (e.g., harvested), or applied to the field(e.g., sprayed), changes as the vehicle travels over the field. This canaffect a number of things. For example, as the vehicle travels over thesoil, it can inflict damage on the soil, such as undesired levels ofcompaction, among other things. Similarly, heavier vehicles may be morelikely to become stuck in muddy areas or other areas within the field.

Some of these types of machines have a tire inflation system which canvary the inflation pressure in the tires of the vehicle. Other machineshave a traction control system which can vary the torque applied to theground engaging elements (e.g., wheels, tracks, etc.) different axels ofthe machine in order to increase traction.

In addition, the soil cone index is a measure of the strength of thesoil, or a measure of the ability of the soil to carry a load. A conepenetrometer is a device which measures the force that it takes to pushan element of the cone penetrometer tool into the soil. Thus, the conepenetrometer tool provides a soil cone index which indicates the abilityof the soil to bear a load, and can also be an index indicative of thelevel of compaction of the soil.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

A soil measure, such as a soil cone index, and a vehicle indexindicating the amount of force the vehicle exerts on the ground as ittravels over the ground, are obtained and compared to identify a soildamage score. The soil damage score can be mapped over a field and anagricultural vehicle can be controlled based upon the soil damage score.

Example 1 is a computer implemented method of controlling anagricultural vehicle, comprising:

-   obtaining a soil measure for soil indicative of an ability of the    soil to bear a load;-   obtaining a vehicle index indicative of a force imparted by an    agricultural vehicle on the soil, as a load carried by the    agricultural vehicle varies as the agricultural vehicle travels    along a travel path over a field;-   identifying a soil damage value based on the soil measure and the    vehicle index; and-   generating a control signal to control a controllable subsystem on    the agricultural vehicle based on the soil damage value.

Example 2 is the computer implemented method of any or all previousexamples and further comprising:

generating a map of soil damage values over the field.

Example 3 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating a control signal to control a path planning subsystem toperform path planning to identify a recommended travel path through thefield based on the map of soil damage values.

Example 4 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a path planningsubsystem comprises:

generating a control signal to control a vehicle navigation subsystem toautomatically navigate the agricultural vehicle over the recommendedtravel path.

Example 5 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a path planningsubsystem comprises:

generating a control signal to control a vehicle display to display therecommended travel path to an operator for navigating the agriculturalvehicle over the recommended travel path.

Example 6 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating a control signal to control a tire inflation subsystem tocontrol tire inflation pressure in tires on the agricultural vehicle.

Example 7 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating a load redistribution signal to control a load redistributionsubsystem to redistribute the load carried by the agricultural vehiclebased on the soil damage value.

Example 8 is the computer implemented method of any or all previousexamples wherein generating a load redistribution signal comprises:

generating a ballast control signal to control a ballast controlsubsystem to redistribute ballast on the agricultural vehicle.

Example 9 is the computer implemented method of any or all previousexamples wherein generating a load redistribution signal comprises:

generating a frame control signal to control a frame reconfigurationsubsystem to reconfigure a frame of the agricultural vehicle toredistribute the load carried by the agricultural vehicle.

Example 10 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating a traction control signal to control a traction controlsubsystem to control an amount of torque applied to a ground engagingelement of the agricultural machine.

Example 11 is the computer implemented method of any or all previousexamples wherein generating a traction control signal comprises:

generating an axle-based traction control signal to control anaxle-based traction control subsystem to control an amount of torqueapplied to axles on the agricultural vehicle on an individual axlebasis.

Example 12 is the computer implemented method of any or all previousexamples wherein the ground engaging elements are wheels and whereingenerating a traction control signal comprises:

generating a wheel-based traction control signal to control awheel-based traction controller that controls an amount of torqueapplied to each of the wheels individually.

Example 13 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating an interface control signal to control an operator interfacesubsystem to display the soil damage score to an operator.

Example 14 is the computer implemented method of any or all previousexamples wherein generating a control signal to control a controllablesubsystem comprises:

generating a machine settings control signal to control a machinesettings subsystem to control machine settings on the agriculturalvehicle.

Example 15 is an agricultural system, comprising:

-   at least one processor;-   a data store storing computer executable instructions which, when    executed by the at least one processor, cause the at least one    processor to perform steps, comprising:-   obtaining a soil measure for soil indicative of an ability of the    soil to bear a load;-   obtaining a vehicle index indicative of a force imparted by an    agricultural vehicle on the soil, as a load carried by the    agricultural vehicle varies as the agricultural vehicle travels    along a travel path over a field;-   identifying a soil damage value based on the soil measure and the    vehicle index; and-   generating a control signal to control a controllable subsystem on    the agricultural vehicle based on the soil damage value.

Example 16 is the agricultural system of any or all previous examplesand further comprising:

generating a map of soil damage values over the field.

Example 17 is the agricultural system of any or all previous exampleswherein generating a control signal to control a controllable subsystemcomprises:

generating a control signal to control a path planning subsystem toperform path planning to identify a recommended travel path through thefield based on the map of soil damage values.

Example 18 is the agricultural system of any or all previous exampleswherein generating a control signal to control a controllable subsystemcomprises:

generating a control signal to control a tire inflation subsystem tocontrol tire inflation pressure in tires on the agricultural vehicle.

Example 19 is the agricultural system of any or all previous exampleswherein generating a control signal to control a controllable subsystemcomprises:

generating a load redistribution signal to control a load redistributionsubsystem to redistribute the load carried by the agricultural vehiclebased on the soil damage value.

Example 20 is a computer implemented method of controlling anagricultural vehicle, comprising:

-   obtaining a soil measure for soil indicative of an ability of the    soil to bear a load;-   obtaining a vehicle index indicative of a force imparted by an    agricultural vehicle on the soil, as a load carried by the    agricultural vehicle varies as the agricultural vehicle travels    along a travel path over a field;-   generating a map of a soil damage value over the field, the soil    damage value being based on the soil measure and the vehicle index;    and-   generating a control signal to control a controllable subsystem on    the agricultural vehicle based on the soil damage value.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one example of an agricultural system.

FIG. 2 is a block diagram showing one example of a soil damage computingsystem.

FIG. 3 is a flow diagram illustrating one example of the overalloperation of an agricultural system.

FIG. 4 is a flow diagram illustrating one example of the operation of apath planning system in generating a path plan based upon a soil damagescore.

FIG. 5 is a flow diagram illustrating one example of the operation of asoil measure identifying system in predicting a soil cone index orsimilar measure.

FIG. 6 is a flow diagram illustrating one example of the operation ofthe soil damage computing system in correlating a soil damage score to aconsequence, such as a change in productivity.

FIG. 7 is a block diagram of one example of the agricultural systemdeployed in a remote server architecture.

FIGS. 8-10 are block diagrams of mobile devices that can be used in thearchitectures and systems and machines shown in previous FIGS.

FIG. 11 is a block diagram of one example of a computing environmentwhich can be used in the agricultural system and other systems andmachines shown in previous FIGS.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of one example of an agricultural system 100.Agricultural system 100 includes a set of agricultural machines 102-104that are operated by operators 106-108, respectively. In the exampleshown in FIG. 1 , a soil damage computing system 110 is showncommunicating with agricultural machines 102-104, and other systems 112,over network 114. It will be noted that soil damage computing system 110can be deployed on one or more of the agricultural machines 102-104 oron other systems 112 as well, but it is shown as a separate system thatis accessed over network 114 by agricultural machines 102-104. System110 can be distributed among various machines and locations as well.

Network 114 can be a local area network, a wide area network, a cellularcommunication network, a near field communication, or any of a widevariety of other networks or combinations of networks. The other systems112 can be farm managers systems, vendor systems, manufacture systems,or any of a wide variety of other computing systems.

Agricultural machine 102 illustratively includes location sensor 116,soil measure sensor 118 (which can include cone potentiometer 120,downforce/margin system 122 and other items 124), terrain sensor 126,moisture sensor 128, soil type sensor 130, vehicle index data/sensors132, controllable subsystems 134, soil damage computing system 136,control system 137, and other agricultural machine functionality 138.Agricultural machines 102-104 can be any of a wide variety of differenttypes of agricultural machines. In some examples, machines 102-104 aremachines that vary in weight as they travel over an agricultural fieldperforming an agricultural operation. Such agricultural machines orvehicles can be agricultural machines, such as planting machines (whichvary in weight based on seeding rate), material application systems(such as a sprayer which varies in weight as it applies material to afield), a harvester (which varies in weight at it harvests material inthe field), and any of a wide variety of other machines. Location sensor116 can be a global navigation satellite system (GNSS) receiver oranother location sensor that senses the geographic location ofagricultural machine 102. Such sensors can also include a dead reckoningsystem, or any of a wide variety of other sensors.

Terrain sensor 126 can sense the type of terrain (such as the terrainelevation, slope, etc.). Sensors 126 can be a gyroscopic sensor, anaccelerometer, or any of a wide variety of other inertial measurementunits, or terrain sensors that can sense the orientation of agriculturalmachine 102 as it travels through the field.

Moisture sensor 128 illustratively senses the moisture of the soil.Moisture sensor 128 can be a sensor probe mounted to machine 102 toengage the soil, or another sensor 128. Soil type sensor 130 isillustratively a sensor that senses the type of soil over which machine102 is traveling. In one example, soil samples are taken and analyzedand then geographically correlated to the field. In other examples, soiltype sensor 130 can be a sensor disposed on a different machine thatsamples and senses the soil type during a prior operation. Other soiltype sensors can be used as well.

Soil measure sensor 118 generates a measure indicative of the ability ofthe soil to support a load. In one example, sensor 118 can be a conepenetrometer 120. Cone penetrometer 120 can be a mechanism thatpenetrates the soil during the operation of machine 102 and generates anoutput indicative of a cone index value. The cone index value for thesoil is a measure of the resistance to penetration of the soil.Downforce/margin system 122 generates a proxy indicative of the coneindex or otherwise indicative of the ability of the soil to bare a load.For instance, the downforce/margin system 122 can measure the downforceexerted by a row unit of a planter and reduce the measured downforce bythe downforce margin which is the load borne by the gauge wheels of arow unit. This is indicative of the overall resistance imparted on thesoil by the row unit and may be a proxy indicative of the cone index orotherwise indicative of the ability of the soil to bear a load.

Vehicle index data/sensors 132 sense a variable or other item that canbe used to calculate a vehicle index indicative of the amount of force(e.g., in pounds per square inch or in other units) that machine 102will exert on the soil as it travels over the soil. The vehicle indexdata/sensors 132 can be pre-stored data indicative of the weight of themachine, and indicative of how the weight of the machine will vary overits full-to-empty, or empty-to-full cycle. In another example, thedata/sensors 132 can be sensors that sense the actual weight of thevehicle (such as load sensors in the axels of the vehicle, etc.) oranother variable that is indicative of the force that the vehicle willimpart to the ground as it travels over the field.

Controllable subsystems 134 on machine 102 can include such things as avehicle navigation subsystem, a tire inflation subsystem, a loadre-distribution subsystem, an operator interface subsystem, a tractioncontrol subsystem, a communication subsystem, a machine settingsubsystem, among others. Soil damage computing system 136 can obtain thesoil measure generated by soil measure sensor 118 indicative of thesoils ability to support a load, and the vehicle index generated byvehicle index data/sensors 132 indicative of the force or load thatmachine 102 will apply to the soil. Based upon these values, soil damagecomputing system 136 can compute a soil damage metric indicative of acompaction of the soil, or other item of soil damage that will beimparted, or is being imparted, by machine 102 as machine 102 istraveling over the field.

It will be noted that soil damage computing system 136 need notnecessarily reside on agricultural machine 102, but can be separate frommachine 102 (as indicated by soil damage computing system 110) andaccessed over network 114. In one example, soil damage computing system136 or 110 can generate a near real time soil damage metric indicativeof the soil damage (if any) being imparted by machine 102 on the soilover which it is traveling. In another example, the soil damagecomputing system 136, 110 can generate a predictive value indicative ofthe predicted soil damage that machine 102 will impart on the soil if ittravels over the soil in the future.

Based on the soil damage metric, control system 137 can generate controlsignals to control the controllable subsystems 134. For instance, wherethe soil damage metrics indicate a relatively large degree of soildamage (compared to a threshold value input by the user or a defaultthreshold or another threshold), control system 137 can generate controlsignals to control the tire inflation subsystem to deflate the tires toincrease the contact patch between the tire and the ground, and thus tospread the force imparted by machine 102 on the soil over a large area.In another example, control system 137 can generate control signals tooutput a display or other operator output that can be used to notify theoperator of the level of damage that is being, or will be, imparted onthe soil. In yet another example, control system 137 can control anavigation system to engage a path planning system to generate arecommended path through the field that will reduce the overall damageto the soil. For instance, the path planning system may generate a paththat has machine 102 traveling over particularly susceptible spots inthe field (such as low spots, muddy spots, etc.) when machine 102 iscloser to empty than full so that machine 102 is imparting less force onthe field. In another example, the path planning system may plan a paththat avoids the vulnerable areas until later in the day (such as whenthey dry out), etc. These are just examples and other examples arecontemplated herein as well. In yet another example, control system 137can communicate the soil damage metric to other agricultural machines104, to other systems 112, etc.

FIG. 2 is a block diagram showing one example of the soil damagecomputing system in more detail. For purposes of the present discussion,it will be assumed that soil damage computing system 110 is being shownand described in FIG. 2 . Also, soil damage computing systems 110 and136 can be similar or different. For purposes of the present descriptionit will be assumed that they are similar so that only soil damagecomputing system 110 is described in more detail.

FIG. 2 shows that, in one example, soil damage computing system 110 caninclude one or more processors or servers 140, data store 142, vehicleindex identification system 144, soil damage score generator system 146,soil measure identification system 148, path planning system 150,feedback processing system 152, control signal generator 154, and othercomputing system functionality 156. Data store 142 can store vehiclecharacteristics 158, load varying characteristics 160, maps 162, andother items 164. Vehicle index identification system 144 can includedata store interaction system 166, runtime data processor 168, vehicleindex generator/estimator 170, and other items 172. Soil damage scoregeneration system 146 can include soil measure/vehicle index comparisonsystem 174, threshold comparison system 176, soil damage score outputsystem 178, mapping system 180, and other items 182.

Soil measure identification system 148 can include cone index signalprocessor 184, proxy signal processor 186, soil measure predictionsystem 188, and other items 190. Soil measure prediction system 188 caninclude terrain identifier 192, soil type identifier 194, soil measureidentifier 196, score generator 198, and other items 200. Path planningsystem 150 can include optimization criteria accessing system 202, cycleidentifier 204, and path processing model 206 (which can include fillstrategy/machine setting variation system 208, traction controlvariation system 210, recommended path identifier 212, and other items214). Path planning system 150 can include recommended path damageassessment system 214, suggested path and settings output system 216,and other items 218.

FIG. 2 also shows that controllable subsystems 134 can include vehiclenavigation subsystem 220, tire inflation subsystem 222, loadredistribution subsystem 224 (which can include ballast control system226, frame configuration system 228, and other items 230), operatorinterface subsystem 232, traction control subsystem 234 (which caninclude axel-based controller 236, wheel-based controller 238 and otheritems 240), communication subsystem 242, machine settings subsystem 244,and other items 246. Before describing soil damage computing system 110,and its operation, in more detail, a description of some of the items insoil damage computing system 110, and their operation, will first beprovided.

Vehicle characteristics 158 can include the physical dimensions of thevehicle, the weight of the vehicle, the model and make of the vehicle,among other things. Load varying characteristics 160 can be dataindicative of how the load carried by the vehicle varies throughout itsfull-to-empty or empty-to-full cycle. For instance, load varyingcharacteristics may include a lookup table, a curve, or other model ordata that indicate how quickly the load of a seeding machine drops as itis seeding at a particular seed population rate, at a particular groundspeed, etc. The load varying characteristics 160, in another example,indicate how the load of a harvester increases as it is harvesting aparticular hybrid, with a moisture level, in a field that has aparticular estimated yield, at a particular harvester speed, etc. Theseare examples only and a wide variety of other models, data structures,or mechanisms can be used to indicate that load varying characteristics160 of a vehicle. Maps 162 may include terrain maps, soil type maps,yield maps, soil measure maps, soil damage maps, moisture maps, vehicleindex maps, or other maps that indicate characteristics of the field,characteristics of the machine as those characteristics vary over thefield, or other information.

Vehicle index identification system 144 generates the vehicle indexwhich indicates the amount of force that the vehicle or machine 102 willexert on the soil over which it is traveling. Data store interactionsystem 166 can interact with data store 142 to obtain the vehiclecharacteristics 158 and/or the load varying characteristics 160 and/ormaps 162. Runtime data processor 168 can obtain runtime information fromvehicle index data/sensors 132 that may be used to derive the vehicleindex value for machine 102. Vehicle index generator/estimator 170 canthen either generate the vehicle index value, or estimate that value,based upon the information obtained. For instance, when runtime dataprocessor 168 is generating data indicative of how the load of thevehicle is changing over time, vehicle index generator 170 can use thatinformation in conjunction with the vehicle characteristics 158 and/orother information to generate a vehicle index value indicative of theactual load being imparted on the soil by the agricultural machine. Whenthe information is indicative of how the load of the vehicle will changein the future during its empty-to-full or full-to-empty cycle, thenvehicle index generator/estimator 170 can estimate the vehicle indexvalue at a time in the future, or at a location in the field, etc.

Soil measure identification system 148 generates a soil measure valueindicative of the ability of the soil to bear a load. Cone index signalprocessor 184 can receive the signal from cone penetrometer 120 andprocess that signal to obtain a cone index value indicative of the coneindex value for the soil sensed by cone index penetrometer 120. Proxysignal processor 186 can receive the signal from a proxy of the coneindex (e.g., from downforce/margin system 122) and process that signalto identify the soil measure value from the proxy signal.

Soil measure prediction system 188 can generate a prediction of the soilmeasure at different points over the field, based upon the informationgenerated by a plurality of different sensors, or generated in otherways. For instance, terrain identifier 192 can identify the type ofterrain lin the field based on a signal from terrain sensor 126 (shownin FIG. 1 ) or may obtain the terrain information from maps 162 or inother ways. Soil type identifier 194 can obtain a soil type at differentlocations in the field from a soil type sensor 130 or from soil maps 162or in other ways. Soil moisture identifier 196 can obtain the soilmoisture values for soil at different locations in the field frommoisture sensor 128 or from soil moisture maps 162 or in other ways. Forinstance, based upon the terrain, soil moisture identifier 196 mayidentify low spots. Based on weather information, such as precipitationinformation, sun information, temperature information, wind information,etc. soil moisture identifier 196 can estimate soil moisture atdifferent locations in the field. Score generator 198 can generate thesoil measure value indicative of the ability of the soil to bear a loadbased upon the information from identifiers 192, 194, 196, and/or otherinformation generated by other items 200.

Soil damage score generation system 146 obtains the vehicle index fromvehicle index identification system 144 and the soil measure from soilmeasure identification system 148 and generates a soil damage scorewhich can be used by path planning system 150 and/or control signalgenerator 154 to generate control signals for controlling controllablesubsystem 134.

Soil measure/vehicle index comparison system illustratively converts thesoil measure and the vehicle index to comparable units (such as poundsper square inch, etc.) and compares the soil measure to the vehicleindex to determine whether the vehicle will damage the soil. Forinstance, if the vehicle index exceeds the soil measure, this means thatthe force that the vehicle will exert on the soil exceeds the ability ofthe soil to bear a load, and thus will result in compaction. However,some compaction may be acceptable. Therefore, threshold comparisonsystem 176 determines whether the amount by which the vehicle indexexceeds the soil measure meets a threshold level. The threshold levelmay indicate when undesirable soil damage occurs. The threshold levelmay be input by the operator, it may be empirically determined, it maybe a default or dynamically changing value, among other things.

Mapping system 180 can generate a map of the soil damage scores. The mapmay be of scores from actual sensed values, or a predictive map thatpredicts the soil damage scores based upon the load variation of theagricultural machine as it travels over the field (and thus based on itsvarying vehicle index values) and based upon the predicted soil measuresof the soil in the field generated by soil measure prediction system188. The soil damage scores can be stored in maps or in other ways aswell and soil damage score output system 178 can generate an outputindicative of the soil damage scores. For instance, soil damage scoreoutput system 178 may provide an output to path planning system 150and/or control signal generator 154. Soil damage score output system 178can provide an output to other computing system functionality 156 aswell.

Path planning system 150 can receive the output from soil damage scoreoutput system 178 and perform path planning to identify paths that themachine should take through the field when performing its operation. Inanother example, path planning system 150 may generate a timing orscheduling output indicative of when the machine should perform its pathor other outputs as well.

Optimization criteria accessing system 202 identifies the optimizationcriteria that are to be used in path planning. For instance, theoptimization criteria may be stored in data store 142 or elsewhere. Theoptimization criteria may be input by the operator, or they may bedefault criteria. The optimization criteria may be dynamically changedor set in other ways. By way of example, it may be that the pathplanning system is to calculate a path for the agricultural machinethrough the field optimizing productivity. In another example, the pathmay be calculated optimizing the soil damage score (to reduce soildamage wherever possible). The optimization criteria may be to plan theagricultural operation to take place as quickly as possible, thusoptimizing speed. The optimization criteria accessing system 202 mayaccess optimization criteria in other ways, and those criteria may beother criteria as well.

Cycle identifier 204 identifies the full-to-empty cycle of theagricultural machine (or the empty-to-full cycle where appropriate).Identifier 204 may identify the distance that the machine can travelduring the cycle, the time the machine will take to travel over thatcycle, or other characteristics or parameters of the full-to-empty cycleor the empty-to-full cycle of the agricultural machine. Once theoptimization criteria are known, and the cycle of the agriculturalmachine is known, then path processing model 206 performs pathprocessing to identify a recommended path through the field for theagricultural machine.

Path processing model 206 can be any type of model that generates a pathoutput to optimize the optimization criteria. In generating the path,model 206 varies different variables, such as the geographic location ofthe path, the traction control that is used, the fill strategy andmachine settings that are used by the machine (such as how full themachine is filled and when it is unloaded during different paths, etc.),among other things. By way of example, fill strategies/machine settingsvariation system 208 varies the fill strategies and machine settings sothat path processing model 206 can model paths, optimizing on theoptimization criteria, with different fill strategies in differentmachine settings. Traction control variation system 210 varies thetraction control strategies or settings that are used to controltraction on the machine so that model 206 can model different pathsthrough the field, optimizing based upon the optimization criteria,using different traction control settings or traction controlstrategies. Other items can be varied so that model 206 can modeldifferent paths through the field with other variations as well.Recommended path identifier 212 identifies the recommended path, with arecommended fill strategy and set of machine settings, as well astraction control variations that were modeled.

Recommended path damage assessment system 214 then analyzes therecommended path to access the soil damage that will be created by themachine, if it follows the recommended path. For instance, it may bethat path processing model 206 outputs the recommended path, but eventhe recommended path may cause an undesirable amount of damage.Therefore, system 214 assesses the soil damage that will be inflicted bythe machine, and can generate an output indicative of the damage.Recommended path and settings output system 216 then generates an outputindicative of the recommended path and the recommended settings (e.g.,fill strategy, machine settings, traction control strategy and settings,etc.).

System 216 can also output an indication of the damage that will beinflicted, as determined by recommended path damage assessment system214. System 216 may output the recommended path as a navigational pathindicating the geographic path that the machine is to take in order tofollow the recommended path. The recommended path can be output to anavigation system which can automatically navigate the machine along thepath, or it can be output to an operator so that the operator canmanually navigate the machine over that path, or it can be output inother ways. Similarly, the recommended settings can be output so thatthey can be automatically set on the machine or set by an operator, etc.

Control signal generator 154 can receive the recommended path andsettings output by system 216 and generate control signals to controlcontrollable subsystems 134 based upon the recommended path andsettings. For instance, control signal generator 154 can generate outputsignals to control vehicle navigation subsystem 280 to automaticallynavigate the agricultural machine through the recommended path. Wherethe settings include tire inflation settings, then control signalgenerator 154 can generate control signals to control tire inflationsubsystem 222 to automatically inflate and deflate the tires, as theagricultural machine travels along the recommended path, based upon thetire inflation settings.

It may also be that the recommended settings are settings for a loadredistribution subsystem 224 that can be used to redistribute the loadon the agricultural machine about its frame. Therefore, control signalgenerator 154 can generate control signals to accomplish the desiredload redistribution using subsystem 224. By way of example, it may bethat the agricultural vehicle is configured with a ballast controlsignal system 226 that can mechanically move ballast about theagricultural machine to change where the load imparted by the machine isimparted to the soil over which it is traveling. Control signalgenerator 154 can generate control signals to control ballast controlsystem 226 to redistribute the ballast on the machine based upon therecommended settings. Frame configuration system 228 can be controlledto reconfigure the frame of the agricultural machine, such as tocollapse the machine, expand the machine, etc., to change the way theload from the machine is imparted to the field over which it istraveling. The frame configuration system 228 can use hydraulic orpneumatic cylinders or other electrical, mechanical, pneumatic,hydraulic, or other actuators to change the configuration of the machineframe. The control signals generated by control signal generator 154 canbe used to control those actuators to move the frame to a desiredconfiguration.

Traction control system 234 can use axel-based controllers 236 tocontrol the torque applied by individual axels on the agriculturalmachine. Wheel-based controller 238 can be used to control the torqueapplied by individual wheels or individual tracks or other individualground engaging mechanisms. Therefore, control signal generator 154 cangenerate control signals to control the axel-based controller 236 and/orthe wheel-based controller 238 to perform traction control on theagricultural vehicle based upon the recommended settings.

Operator interface subsystem 232 can include any operator interfacesubsystems that the operator 106 can use to control agricultural machine102, such as a steering wheel, joysticks, levers, linkages, pedals,buttons, a touch sensitive display screen or another display screen, amicrophone and speaker (where speech synthesis and speech recognitionare used), or a wide variety of other audio, visual, and haptic userinterface elements. Operator interface subsystem 232 can thus display orotherwise communicate the recommended path and settings to the operatorand thus control signal generator 154 can generate control signals tocontrol operator interface subsystem 232 to perform that type ofcommunication with the operator.

Communication subsystem 242 can be controlled to communicate therecommended path and settings to other systems 112, other agriculturalmachines 104, etc. Machines setting subsystem 246 can be used toautomatically set the machine settings to the suggested settings.Therefore, control signal generator 154 can generate control signals tocontrol machine settings subsystem 244 to set the machine settings tothe recommended settings.

FIG. 3 is a flow diagram illustrating one example of the overalloperation of the agricultural system 100, in controlling agriculturalmachine 102 based upon a soil damage score that is calculated by soildamage score generation system 146. Soil damage score generation system146 obtains a soil measure indicative of the ability of the soil in afield to carry a load from soil measure identification system 148.Obtaining the soil measure is indicated by block 250 in the flow diagramof FIG. 3 . The soil measure can be a cone index score 252, a proxy 254,such as a signal based on the downforce, less the downforce margin, etc.The soil measure can be based on a runtime measurement taken (such asfrom a cone penetrometer 120, as indicated by block 256) or the soilmeasure can be a predicted value based upon field characteristics, suchas the terrain, soil type, moisture, etc., as indicated by block 258.The soil measure may be a map of soil measure scores through the field,as indicated by block 260, or it can be provided in a wide variety ofother ways, as indicated by block 262.

Soil damage score generation system 146 then determines a vehicle index.The vehicle index can account for the variation in the load based uponthe load varying characteristics 160 in data store 142, or in otherways. For instance, system 146 can obtain the vehicle index from vehicleindex identification system 144. The vehicle index is indicative of theforce that the vehicle will exert on the ground as the vehicle travelsover the ground through the full-to-empty cycle or the empty-to-fullcycle, as indicated by block 264 in the flow diagram of FIG. 3 . Thevehicle index identification system 144 can generate the vehicle indexaccounting for the varying load that the vehicle will carry as ittravels over the field, as indicated by block 266. The vehicle index cantake into account the vehicle characteristics 158, as indicated by block268, and other items as indicated by block 270. Soil measure/vehicleindex comparison system 174 then compares the soil measure to thevehicle index to obtain a soil damage score, as indicated by block 272.Threshold comparison system 176 determines whether the soil damage scoreexceeds a threshold value, as indicated by block 274. Mapping system 180can generate the soil damage score over the entire field, as indicatedby block 276, and the soil damage score can be compared against athreshold in other ways as well, as indicated by block 278.

Control signal generator 154 can then generate control signals tocontrol subsystems 134 of the agricultural machine, as it crosses thefield. Controlling the vehicle is indicated by block 280 in the flowdiagram of FIG. 3 . The vehicle can be controlled automatically or basedon operator preference or default values, as indicated by block 282. Thesoil damage scores can be surfaced for the operator, as indicated byblock 284, and control signal generator 154 can make a go/no go decisionand surface that for the operator through operator interface subsystem282, as indicated by block 286. For instance, control signal generator154 may determine that the damage scores are so high that the machineshould not perform the agricultural operation until a later time whenthe soil stabilizes, or firms up, or under other circumstances. This canbe displayed for the user, or otherwise surfaced for the user, throughan operator interface subsystem 232.

Path planning system 150 can perform path planning based on the damagescores, as indicated by block 288. The path processing model 206 cangenerate a recommended fill strategy, as indicated by block 290, andthat recommended fill strategy can be output to the operator or to themachine for automatic control as well.

Tire inflation subsystem 222 can be controlled to control the tireinflation based upon the soil damage scores, as indicated by block 292.Traction control system 234 can be controlled to control the tractioncontrol on an individual axel basis or on an individual wheel basis, asindicated by block 294. Load redistribution subsystem 224 can becontrolled to control the ballast position or other load distribution,as indicated by block 296. Communication subsystem 242 can be used tocommunicate the recommended path, the soil damage scores, therecommended machine settings, etc., to other machines or other systems,as indicated by block 298. The machine can be controlled in a widevariety of other ways as well, as indicated by block 300.

It should also be noted that, in one example, the values that have beenestimated or predicted by soil damage computing system 110 can bemodified by feedback processing system 152 which may obtain actual,measured values and perform machine learning on the variousfunctionality in soil damage computing system 110 that generatesestimates or predictions to improve the accuracy of those estimates orpredictions. Similarly, estimated or predicted values can be modified orcalibrated by feedback processing system 152 based upon actual measuredvalues as well. Feeding back any runtime sensed values for machinelearning and/or calibration is indicated by block 302 in the flowdiagram of FIG. 3 . For instance, where soil measure prediction system188 predicts a cone index value or other soil measure, then a measuredcone index value may be fed back, for processing by feedback processing152 so that the algorithm used by soil measure prediction system 188 canbe trained using machine learning or other techniques for more accuracy.Also, other predicted values can be modified or calibrated to improvetheir accuracy based on the actual, measured value(s). Performingfeedback to the soil measure identification system is indicated by block304 in the flow diagram of FIG. 3 .

Also, in an example in which vehicle index generator/estimator 170generates an estimated value for the vehicle, a vehicle index sensor 132may sense the actual vehicle index value (such as the weight of thevehicle, etc.). The actual sensed value may be fed back to feedbackprocessing system 152 for processing using machine learning or otheralgorithms to improve the estimation generated by vehicle indexgenerator/estimator 170. Estimated values can be calibrated based on thefed back values as well. Feeding the information back for improving theaccuracy of vehicle index identification system 144 is indicated byblock 306 in the flow diagram of FIG. 3 .

These or other measured values may also be fed back to soil damage scoregeneration system 146 to improve the accuracy of that system, asindicated by block 308. Measured values can also be fed back to pathplanning system 150 to improve the path planning as indicated by block310. For instance, where path planning system 150 generates arecommended path and recommended settings, those settings and thecharacteristics of the path can be sensed and fed back to path planningsystem 150 for machine learning to improve path planning and recommendedsettings. The measured values can be processed by feedback processingsystem 152 to improve the control signals generated by control signalgenerator 154 as well, as indicated by block 312. Measured values can befed back in other ways, for use by other machine learning or calibrationalgorithms as well, as indicated by block 314.

FIG. 4 is a flow diagram illustrating one example of the operation ofpath planning system 150, in more detail. It is first assumed that soildamage score generator system 146 uses mapping system 180 to generate amap of soil damage values over the field upon which the machine will beperforming in agricultural operation. Generating a map of the soildamage values over the field is indicated by block 316 in the flowdiagram of FIG. 4 . Optimization criteria accessing system 202 thendetermines the optimization criteria that are to be used by pathplanning system 150 in identifying a recommended path. Determining theoptimization criteria is indicated by block 318 in the flow diagram ofFIG. 4 . The optimization criteria may be determined based on anoperator input, the optimization criteria may be determined dynamically,or the optimization criteria may be default values that are obtainedfrom data store 142, as indicated by block 320. The optimizationcriteria may be productivity 322, or agronomics (such as soil damage)324. The optimization criteria may call for a balance betweenproductivity and agronomics, as indicated by block 326. The optimizationcriteria may be other criteria and they may be obtained in other ways aswell, as indicated by block 328.

Path processing model 206 then models or otherwise evaluates differentpaths through the field based on the soil damage values given theoptimization criteria, as indicated by block 330. The different pathsmay be evaluated by varying the fill strategies as indicated by block332 and by varying the machine settings (such as tire inflationpressure, traction control, ballast or load redistribution, etc.), asindicated by block 334. The various paths through the field can beevaluated based on the optimization criteria by varying the timing whenthe agricultural machine will be at different points in the field, asindicated by block 336. The different paths can be modeled or evaluatedby varying a wide variety of other parameters, and in a wide variety ofother ways, as indicated by block 338.

Recommended path identifier 212 then identifies on or more recommendedpaths, as indicated by block 340. In one example, recommended pathidentifier 212 identifies a plurality of different recommended pathsthat are ranked based on the optimization criteria, and are output asdifferent selectable paths. Outputting the ranked paths as differentselectable paths based on the optimization criteria is indicated byblock 342. Recommended path identifier 212 may output the recommendedpath as a single path, as indicated by block 344, or in other ways, asindicated by block 346.

Recommended path damage assessment system 214 then determines whether athreshold amount of soil damage is likely over the field if the vehiclefollows the recommended path. Again, the threshold can be input by theoperator, it can be a default threshold, or it can be a dynamicallydetermined threshold or another threshold. Determining whether athreshold amount of soil damage is likely over the field is indicated byblock 348 in the flow diagram of FIG. 4 .

If so, then an indication that the threshold amount of soil damage islikely to occur is output to control signal generator 154 whichgenerates an output on operator interface subsystem 232 notifying theoperator that a threshold amount of damage will occur over the field, asindicated by block 350. The output may be a map of the likely soildamage, as indicated by block 352, or the output can be a simple go/nogo indicator indicating that the agricultural operation should not beperformed at this time, as indicated by block 354. The output may be anindication that the operation should be delayed for a certain amount oftime, as indicated by block 356. The output may include an overrideactuator so that the operator can override the output, as indicated byblock 358, and then continue to perform the agricultural operation. Theoutput notifying the operator can be any of a wide variety of otheroutputs notifying the operator in other ways as well, as indicated byblock 360.

Assuming that a threshold amount of soil damage is not likely to occurover the field when navigating through the recommended path, then therecommended path and settings output system 216 generates an outputindicative of the recommended path and recommended settings, asindicated by block 362. In one example, if damage is unlikely, then therecommended path is optimized based on criteria other than damage, suchas productivity, as indicated by block 364. If the damage is likely insome sensitive areas, then the recommended path is illustratively a pathwhich plans to have the agricultural vehicle traveling over thosesensitive areas when it has a lower vehicle index. This may includereducing the tire inflation pressure over those areas, it may includedriving the vehicle over those areas when the vehicle is less full thanat other times, or it may include having the vehicle travel over thoseareas later in the day so that the areas have a chance to dry out, andfirm up, etc., as indicated by block 366. The output of the recommendedpath and settings may be generated in other ways as well, as indicatedby block 368. Control signal generator 154 receives the recommended pathand settings output by system 216 and generates control signals tocontrol controllable subsystems 134 so that the agricultural machinetravels through the recommended path, as indicated by block 370. In oneexample, the control signals can be applied to vehicle navigation system220 to automatically control the vehicle to travel through therecommended path. In another example, the control signals can controlthe vehicle navigation subsystem 220 to travel through the recommendedpath semiautomatically (such as controlling the vehicle automaticallyduring a pass through the field and controlling the vehicle manuallyduring turns), or the control signal generator 154 can generate controlsignals to control operator interface subsystem 232 so that the operatorcan manually control the agricultural vehicle to travel over therecommended path. Controlling the agricultural vehicle to travelautomatically, semiautomatically, or manually over the recommended pathis indicated by block 372 in the flow diagram of FIG. 4 .

The control signal generator 154 can control controllable subsystems 134to implement a suggested fill strategy (such as to fill the machinepartially full, to unload the machine with material to be applied afterthe machine is only partially full during harvesting, or to employ otherfill strategies), as indicated by block 374. Similarly, control signalgenerator 154 can generate control signals to control the controllablesubsystems 134 to implement other desired machine control, such as tocontrol tire inflation pressure, traction control, load or ballastredistribution, etc., as the agricultural machine moves through thefield over the recommended path, as indicated by block 376. The controlsignal generator 154 can generate control signals in other ways toperform other control operations as well, as indicated by block 378.

In one example, soil damage computing system 110 then stores therecommended path, the map of the likely soil damage values, the vehicleindex and soil measure values, and any other desired values orinformation corresponding to the recommended path, as indicated by block380. The information can be stored in data store 142 or in othersystems.

FIG. 5 is a flow diagram illustrating one example of the operation ofsoil measure prediction system 188 in predicting a soil measure (such asa cone index) for different geographic areas of a field. Soil measureprediction system 188 first identifies the field for which the coneindex values are to be predicted, as indicated by block 382 in the flowdiagram of FIG. 5 .

Soil type identifier 194 then obtains the soil type distribution acrossthe field, as indicated by block 384. The soil type can be sensed by asensor as indicated by block 386 or it can be obtained from apreexisting map as indicated by block 388, or the soil type can beobtained in other ways as well, as indicated by block 390. Terrainidentifier 192 then obtains terrain indicators indicating the terrain(e.g., slope, elevation, etc.) across the field, as indicated by block392. The terrain can be obtained from an elevation map 394, the terraincan be sensed, or the terrain can be obtained in other ways, asindicated by block 396.

Soil moisture identifier 196 then obtains a moisture level of the soilacross the field, as indicated by block 398. The soil moisture level canbe sensed by soil moisture sensors, as indicated by block 400, or thesoil moisture can be predicted based on historical weather information(such as precipitation information), drainage, and other information, asindicated by block 402. The soil moisture level across the field can beobtained in other ways as well, as indicated by block 404. Scoregenerator 198 then calculates a predicted cone index score (or anothersoil measure indicative of the ability of the soil to support a load)across the field, as indicated by block 406. Score generator 198 can usea score generation model 408, a lookup table, 410, or any of a widevariety of other mechanisms for calculating a predictive cone index orother soil measure score across the field, based upon the soil type, theterrain, the moisture level and/or any other characteristics, asindicated by block 412.

FIG. 6 is a flow diagram illustrating one example of how soil damagecomputing system 110 generates an output indicating the consequences ofinflicting predicted or estimated soil damage on the field. If theoperator is provided with the potential consequences for inflicting thedamage, then the operator may be able to make a more informed choice asto whether to perform the operation, as planned. It is first assumedthat soil damage score generation system 146 calculates the soil damagescore across the field, as indicated by block 414 in the flow diagram ofFIG. 6 . The soil damage score may be based on the soil measurecalculated by soil measure identification system 148 prior to performingthe operation. Also, the soil damage score can be an actually measuredscore based on the soil measure prior to performing the agriculturaloperation and the soil measure after performing the operation, asindicated by block 416. The soil damage score across the field can bepredicted or measured as indicated by block 418, or it can be calculatedin other ways as well, as indicated by block 420.

In the example shown in FIG. 6 , control signal generator 154 controlsthe agricultural machine to travel through the field based on theidentified path, with the identified machine settings. Navigating themachine along the recommended path with the machine settings isindicated by block 422 in the flow diagram of FIG. 6 . The agriculturalmachine illustratively is fitted with a cone index penetrometer oranother device that can be used to detect the soil measure and vehicleindex so that the soil damage score for the different geographiclocations in the field can be verified using actual measurements.Verifying the post-operation soil damage score across the field based onthe actual vehicle path, the vehicle weight, the soil measure, etc., isindicated by block 424 in the flow diagram of FIG. 6 .

The verified soil damage score can be used to calibrate the soil damagescore generation system in generating the soil damage score. Theverified soil damage score can also be used to calibrate the soilmeasure prediction system 188 so that the soil measure can be calibratedas well. Calibrating the predicted soil damage score and soil measurebased upon the verified post-operation soil damage score is indicated byblock 426. The post-operation soil damage score can be obtained in otherways, and used for other processing as well, as indicated by block 428.

Soil damage score output system 178 then identifies a consequence of thedamage, as a damage consequence metric, indicative of the consequence ofthe soil damage. Generating a damage consequence metric is indicated byblock 430 in the flow diagram of FIG. 6 . For example, the yield can becorrelated to the soil damage score across the field to identify a yieldloss in areas of the field that are more highly damaged. Identifying thedamage consequence metric as the yield correlated to soil damage acrossthe field is indicated by block 432 in the flow diagram of FIG. 6 . Thedamage consequence metric can be correlated to plant health as indicatedby normalized difference vegetation index (NDVI) data corresponding tothe field, as indicated by block 434.

The damage consequence metric can be a metric that correlates thetasseling performance of corn (or other vegetation performancecharacteristic) to the soil damage score across the field, or amongdifferent fields, as indicated by block 436. The damage consequencemetric can be generated during subsequent operations (such as operationslater in the season, during subsequent years in the field, orotherwise), as indicated by block 438. The damage consequence metric canbe any of a wide variety of other damage consequence metrics obtained inother ways as well, as indicated by block 440.

Soil damage score output system 178 then also generates an outputindicative of a consequence of inflicting the soil damage on the field,as indicated by block 442. Again, the consequence can be the affect onyield as indicated by block 444, or any of a wide variety of otheroutputs. The output indicative of a consequence of inflicting the soildamage can be stored in data store 142 or another data store, asindicated by block 446. The consequence can be communicated to othersystems as well, as indicated by block 448. The output indicative of aconsequence of inflicting soil damage can be generated in other ways,and be output in other ways as well, as indicated by block 450.

It can thus be seen that the present description provides a mechanism bywhich soil damage due to driving a heavy machine over a soft field canbe measured, predicted, and surfaced for automated control or operatorcontrol. The affect or consequence of the soil damage can also becharacterized and output. Different settings or mechanisms can beautomatically or manually controlled to mitigate predicted soil damageor to avoid operations that will inflict an undesired amount of soildamage on the soil.

The present discussion has mentioned processors and servers. In oneexample, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. They arefunctional parts of the systems or devices to which they belong and areactivated by, and facilitate the functionality of the other componentsor items in those systems.

Also, a number of user interface displays have been discussed. The userinterface displays can take a wide variety of different forms and canhave a wide variety of different user actuatable input mechanismsdisposed thereon. For instance, the user actuatable input mechanisms canbe text boxes, check boxes, icons, links, drop-down menus, search boxes,etc. The mechanisms can also be actuated in a wide variety of differentways. For instance, the mechanisms can be actuated using a point andclick device (such as a track ball or mouse). The mechanisms can beactuated using hardware buttons, switches, a joystick or keyboard, thumbswitches or thumb pads, etc. The mechanisms can also be actuated using avirtual keyboard or other virtual actuators. In addition, where thescreen on which the mechanisms are displayed is a touch sensitivescreen, the mechanisms can be actuated using touch gestures. Also, wherethe device that displays them has speech recognition components, themechanisms can be actuated using speech commands.

A number of data stores have also been discussed. It will be noted theycan each be broken into multiple data stores. All can be local to thesystems accessing them, all can be remote, or some can be local whileothers are remote. All of these configurations are contemplated herein.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used so thefunctionality is performed by fewer components. Also, more blocks can beused with the functionality distributed among more components.

It will be noted that the above discussion has described a variety ofdifferent systems, components and/or logic. It will be appreciated thatsuch systems, components and/or logic can be comprised of hardware items(such as processors and associated memory, or other processingcomponents, some of which are described below) that perform thefunctions associated with those systems, components and/or logic. Inaddition, the systems, components and/or logic can be comprised ofsoftware that is loaded into a memory and is subsequently executed by aprocessor or server, or other computing component, as described below.The systems, components and/or logic can also be comprised of differentcombinations of hardware, software, firmware, etc., some examples ofwhich are described below. These are only some examples of differentstructures that can be used to form the systems, components and/or logicdescribed above. Other structures can be used as well.

FIG. 7 is a block diagram of agricultural machines 102-104, shown inFIG. 1 , except that it communicates with elements in a remote serverarchitecture 500. In an example, remote server architecture 500 canprovide computation, software, data access, and storage services that donot require end-user knowledge of the physical location or configurationof the system that delivers the services. In various examples, remoteservers can deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers candeliver applications over a wide area network and they can be accessedthrough a web browser or any other computing component. Software orcomponents shown in previous FIGS. as well as the corresponding data,can be stored on servers at a remote location. The computing resourcesin a remote server environment can be consolidated at a remote datacenter location or they can be dispersed. Remote server infrastructurescan deliver services through shared data centers, even though theyappear as a single point of access for the user. Thus, the componentsand functions described herein can be provided from a remote server at aremote location using a remote server architecture. Alternatively, thecomponents and functions can be provided from a conventional server, orthe components and functions can be installed on client devicesdirectly, or in other ways.

In the example shown in FIG. 7 , some items are similar to those shownin previous FIGS. and they are similarly numbered. FIG. 7 specificallyshows that soil damage computing system and data store 142 can belocated at a remote server location 502. Therefore, machines 102- 104access those systems through remote server location 502.

FIG. 7 also depicts another example of a remote server architecture.FIG. 7 shows that it is also contemplated that some elements of previousFIGS are disposed at remote server location 502 while others are not. Byway of example, data store 142 or other systems 112 can be disposed at alocation separate from location 502, and accessed through the remoteserver at location 502. Regardless of where the items are located, theycan be accessed directly by harvester 100, through a network (either awide area network or a local area network), the items can be hosted at aremote site by a service, or the items can be provided as a service, oraccessed by a connection service that resides in a remote location.Also, the data can be stored in substantially any location andintermittently accessed by, or forwarded to, interested parties. Forinstance, physical carriers can be used instead of, or in addition to,electromagnetic wave carriers. In such an example, where cell coverageis poor or nonexistent, another mobile machine (such as a fuel truck)can have an automated information collection system. As the machines102-104 come close to the fuel truck for fueling, the systemautomatically collects the information from the machines 102-104 usingany type of ad-hoc wireless connection. The collected information canthen be forwarded to the main network as the fuel truck reaches alocation where there is cellular coverage (or other wireless coverage).For instance, the fuel truck may enter a covered location when travelingto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationcan be stored on the machines 102-104 until the machines 102-104 enter acovered location. The machines 102-104, themselves, can then send theinformation to the main network.

It will also be noted that the elements of previous FIGS., or portionsof them, can be disposed on a wide variety of different devices. Some ofthose devices include servers, desktop computers, laptop computers,tablet computers, or other mobile devices, such as palm top computers,cell phones, smart phones, multimedia players, personal digitalassistants, etc.

FIG. 8 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user’s orclient’s hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of machines 102-104for use in generating,processing, or displaying the data described above. FIGS. 9-10 areexamples of handheld or mobile devices.

FIG. 8 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in previous FIGS., thatinteracts with them, or both. In the device 16, a communications link 13is provided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from previous FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. It can also include, for example, mapping softwareor navigation software that generates desired maps, navigation routesand other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. It can also include computer storagemedia (described below). Memory 21 stores computer readable instructionsthat, when executed by processor 17, cause the processor to performcomputer-implemented steps or functions according to the instructions.Processor 17 can be activated by other components to facilitate theirfunctionality as well.

FIG. 9 shows one example in which device 16 is a tablet computer 600. InFIG. 9 , computer 600 is shown with user interface display screen 602.Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Computer 600 can also use anon-screen virtual keyboard. Of course, computer 600 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 600 can also illustratively receive voice inputs as well.

FIG. 10 shows that the device can be a smart phone 71. Smart phone 71has a touch sensitive display 73 that displays icons or tiles or otheruser input mechanisms 75. Mechanisms 75 can be used by a user to runapplications, make calls, perform data transfer operations, etc. Ingeneral, smart phone 71 is built on a mobile operating system and offersmore advanced computing capability and connectivity than a featurephone.

Note that other forms of the devices 16 are possible.

FIG. 11 is one example of a computing environment in which elements ofprevious FIGS., or parts of it, (for example) can be deployed. Withreference to FIG. 11 , an example system for implementing some examplesincludes a general-purpose computing device in the form of a computer810 programmed to operate as described above. Components of computer 810may include, but are not limited to, a processing unit 820 (which cancomprise processors or servers from previous FIGS.), a system memory830, and a system bus 821 that couples various system componentsincluding the system memory to the processing unit 820. The system bus821 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. Memory and programs described with respectto previous FIGS. can be deployed in corresponding portions of FIG. 11 .

Computer 810 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. It includeshardware storage media including both volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computer 810. Communication media may embody computerreadable instructions, data structures, program modules or other data ina transport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 831and random access memory (RAM) 832. A basic input/output system 833(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 810, such as during start-up, istypically stored in ROM 831. RAM 832 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 820. By way of example, and notlimitation, FIG. 11 illustrates operating system 834, applicationprograms 835, other program modules 836, and program data 837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 11 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 11 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 11 , for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network - CAN, local areanetwork - LAN, or wide area network WAN) to one or more remotecomputers, such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 11 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A computer implemented method of controlling anagricultural vehicle, comprising: obtaining a soil measure for soilindicative of an ability of the soil to bear a load; obtaining a vehicleindex indicative of a force imparted by an agricultural vehicle on thesoil, as a load carried by the agricultural vehicle varies as theagricultural vehicle travels along a travel path over a field;identifying a soil damage value based on the soil measure and thevehicle index; and generating a control signal to control a controllablesubsystem on the agricultural vehicle based on the soil damage value. 2.The computer implemented method of claim 1 and further comprising:generating a map of soil damage values over the field.
 3. The computerimplemented method of claim 2 wherein generating a control signal tocontrol a controllable subsystem comprises: generating a control signalto control a path planning subsystem to perform path planning toidentify a recommended travel path through the field based on the map ofsoil damage values.
 4. The computer implemented method of claim 3wherein generating a control signal to control a path planning subsystemcomprises: generating a control signal to control a vehicle navigationsubsubsystem to automatically navigate the agricultural vehicle over therecommended travel path.
 5. The computer implemented method of claim 3wherein generating a control signal to control a path planning subsystemcomprises: generating a control signal to control a vehicle display todisplay the recommended travel path to an operator for navigating theagricultural vehicle over the recommended travel path.
 6. The computerimplemented method of claim 1 wherein generating a control signal tocontrol a controllable subsystem comprises: generating a control signalto control a tire inflation subsystem to control tire inflation pressurein tires on the agricultural vehicle.
 7. The computer implemented methodof claim 1 wherein generating a control signal to control a controllablesubsystem comprises: generating a load redistribution signal to controla load redistribution subsystem to redistribute the load carried by theagricultural vehicle based on the soil damage value.
 8. The computerimplemented method of claim 7 wherein generating a load redistributionsignal comprises: generating a ballast control signal to control aballast control subsystem to redistribute ballast on the agriculturalvehicle.
 9. The computer implemented method of claim 7 whereingenerating a load redistribution signal comprises: generating a framecontrol signal to control a frame reconfiguration subsystem toreconfigure a frame of the agricultural vehicle to redistribute the loadcarried by the agricultural vehicle.
 10. The computer implemented methodof claim 1 wherein generating a control signal to control a controllablesubsystem comprises: generating a traction control signal to control atraction control subsystem to control an amount of torque applied to aground engaging element of the agricultural machine.
 11. The computerimplemented method of claim 10 wherein generating a traction controlsignal comprises: generating an axle-based traction control signal tocontrol an axle-based traction control subsystem to control an amount oftorque applied to axles on the agricultural vehicle on an individualaxle basis.
 12. The computer implemented method of claim 10 wherein theground engaging elements are wheels and wherein generating a tractioncontrol signal comprises: generating a wheel-based traction controlsignal to control a wheel-based traction controller that controls anamount of torque applied to each of the wheels individually.
 13. Thecomputer implemented method of claim 1 wherein generating a controlsignal to control a controllable subsystem comprises: generating aninterface control signal to control an operator interface subsystem todisplay the soil damage score to an operator.
 14. The computerimplemented method of claim 1 wherein generating a control signal tocontrol a controllable subsystem comprises: generating a machinesettings control signal to control a machine settings subsystem tocontrol machine settings on the agricultural vehicle.
 15. Anagricultural system, comprising: at least one processor; a data storestoring computer executable instructions which, when executed by the atleast one processor, cause the at least one processor to perform steps,comprising: obtaining a soil measure for soil indicative of an abilityof the soil to bear a load; obtaining a vehicle index indicative of aforce imparted by an agricultural vehicle on the soil, as a load carriedby the agricultural vehicle varies as the agricultural vehicle travelsalong a travel path over a field; identifying a soil damage value basedon the soil measure and the vehicle index; and generating a controlsignal to control a controllable subsystem on the agricultural vehiclebased on the soil damage value.
 16. The agricultural system of claim 15and further comprising: generating a map of soil damage values over thefield.
 17. The agricultural system of claim 16 wherein generating acontrol signal to control a controllable subsystem comprises: generatinga control signal to control a path planning subsystem to perform pathplanning to identify a recommended travel path through the field basedon the map of soil damage values.
 18. The agricultural system of claim15 wherein generating a control signal to control a controllablesubsystem comprises: generating a control signal to control a tireinflation subsystem to control tire inflation pressure in tires on theagricultural vehicle.
 19. The agricultural system of claim 15 whereingenerating a control signal to control a controllable subsystemcomprises: generating a load redistribution signal to control a loadredistribution subsystem to redistribute the load carried by theagricultural vehicle based on the soil damage value.
 20. A computerimplemented method of controlling an agricultural vehicle, comprising:obtaining a soil measure for soil indicative of an ability of the soilto bear a load; obtaining a vehicle index indicative of a force impartedby an agricultural vehicle on the soil, as a load carried by theagricultural vehicle varies as the agricultural vehicle travels along atravel path over a field; generating a map of a soil damage value overthe field, the soil damage value being based on the soil measure and thevehicle index; and generating a control signal to control a controllablesubsystem on the agricultural vehicle based on the soil damage value.